Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK
Arun Kejariwal

Arun Kejariwal
Statistical Learning Principal, MZ

@arun_kejariwal

Arun Kejariwal is a statistical learning principal at Machine Zone (MZ), where he leads a team of top-tier researchers and works on research and development of novel techniques for install and click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns. In addition, his team is building novel methods for bot detection, intrusion detection, and real-time anomaly detection. Previously, Arun worked at Twitter, where he developed and open-sourced techniques for anomaly detection and breakout detection. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high-performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.

Sessions

9:0012:30 Tuesday, 22 May 2018
Streaming systems and real-time applications
Location: Capital Suite 15 Level: Intermediate
Arun Kejariwal (MZ), Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio), Sijie Guo (Streamlio)
The need for instant data-driven insights has led the proliferation of messaging and streaming frameworks. In this tutorial, we present an in-depth overview of state-of-the-art streaming architectures, streaming frameworks, and streaming algorithms, covering the typical challenges in modern real-time big data platforms and offering insights on how to address them. Read more.
14:0514:45 Wednesday, 23 May 2018
Data science and machine learning
Location: Capital Suite 12 Level: Intermediate
The rate of growth of data volume and velocity has been accelerating. Further, the variety of data sources also has been growing. This poses a significant challenge in extracting actionable insights in a timely fashion. The talk focuses on how marrying correlation analysis with anomaly detection can help to this end. Also, robust techniques shall be discussed to guide effective decision making. Read more.